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We develop a probabilistic control algorithm, GTLProCo, for swarms of agents with heterogeneous dynamics and objectives, subject to high-level task specifications. The resulting algorithm, agnostic to the number of agents comprising the swarm, not only achieves decentralized control of the swarm but also significantly improves scalability over state-of-the-art existing algorithms.

A shield is attached to a system to guarantee safety by correcting the system’s behavior at runtime. Existing methods that employ …

We develop a control algorithm that ensures the safety, in terms of confinement in a set, of a system with unknown, 2nd-order nonlinear dynamics. The algorithm removes a series of standard, limiting assumptions considered in the related literature since it does not require global boundedness, growth conditions, or a priori approximations of the unknown dynamics’ terms.

We develop DaTaReach and DaTaControl for the reachability analysis and control of systems with a priori unknown nonlinear dynamics. The resulting algorithms not only are suitable for settings with real-time requirements but also provide provable performance guarantees. To this end, they merge noisy data from only a single finite-horizon trajectory and, if available, various forms of side information (from elementary principles) on the underlying dynamics.

We study the synthesis of mode switching protocols for a class of discrete-time switched linear systems in which the mode jumps are governed by Markov decision processes (MDPs). We propose efficient convex optimization-based formulations to find stabilizing policy in the MDP.